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Tomer Shlomi

Researcher at Technion – Israel Institute of Technology

Publications -  75
Citations -  9864

Tomer Shlomi is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Metabolic network & Flux (metabolism). The author has an hindex of 40, co-authored 72 publications receiving 8562 citations. Previous affiliations of Tomer Shlomi include University of California, San Diego & Tel Aviv University.

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Quantitative flux analysis reveals folate-dependent NADPH production

TL;DR: The ability to directly track, by liquid chromatography–mass spectrometry, the passage of deuterium from labelled substrates into NADPH, and combine this approach with carbon labelling and mathematical modelling to measure NADPH fluxes is demonstrated.
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Associating Genes and Protein Complexes with Disease via Network Propagation

TL;DR: A global, network-based method for prioritizing disease genes and inferring protein complex associations, which is called PRINCE, and applies to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus.
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Network-based prediction of human tissue-specific metabolism

TL;DR: This work presents a computational method that successfully describes the tissue specificity of human metabolism on a large scale by integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network.
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A key role for mitochondrial gatekeeper pyruvate dehydrogenase in oncogene-induced senescence

TL;DR: It is shown that the mitochondrial gatekeeper pyruvate dehydrogenase (PDH) is a crucial mediator of senescence induced by BRAFV600E, an oncogene commonly mutated in melanoma and other cancers, and a mechanistic relationship between OIS and a key metabolic signalling axis is revealed, which may be exploited therapeutically.
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Regulatory on/off minimization of metabolic flux changes after genetic perturbations

TL;DR: ROOM is shown to accurately predict steady-state metabolic fluxes that maintain flux linearity, in agreement with experimental flux measurements, and to correctly identify short alternative pathways used for rerouting metabolic flux in response to gene knockouts.